Home > Engineering > Computer Engineering > Volume-3 > Issue-5 > Malware Detection in Android Applications

Malware Detection in Android Applications

Call for Papers

Volume-8 | Issue-3

Last date : 26-Jun-2024

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

First Update : Within 7 Days after submittion

Submit Paper Online

For Author

Research Area


Malware Detection in Android Applications


Mr. Tushar Patil | Prof. Bharti Dhote



Mr. Tushar Patil | Prof. Bharti Dhote "Malware Detection in Android Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5, August 2019, pp.2401-2403, URL: https://www.ijtsrd.com/papers/ijtsrd26449.pdf

Android is a Linux-based operating system used for smart-phone devices. Since 2008, Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016. In this paper, we proposed dynamic malware detection approach for Android applications. In dynamic analysis, system calls are recorded to calculate the density of the system calls. For density calculation, we used two different lengths of system calls that are 3-gram and 5-gram. Furthermore, Naive Bayes algorithm is applied to classify applications as benign or malicious. The proposed algorithm detects malware using 100 real-world samples of benign and malware applications. We observe that proposed method gives effective and accurate results. The 3-gram Naive Bayes algorithm detects 84% malware application correctly and 14% benign application incorrectly. The 5-gram Naive Bayes algorithm detects 88% malware application correctly and 10% benign application incorrectly.

Malware Detection • Naive Bayes Classifier • System Calls • Frequency • Density


IJTSRD26449
Volume-3 | Issue-5, August 2019
2401-2403
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

Thomson Reuters
Google Scholer
Academia.edu

ResearchBib
Scribd.com
archive

PdfSR
issuu
Slideshare

WorldJournalAlerts
Twitter
Linkedin